Leveraging Neural Architecture Search for Custom LLM Inference Pipelines in Heterogeneous Environments
Neural Architecture Search is transforming AI by automating how neural networks are designed, making models faster and more efficient without the tedious trial-and-error process.
Introduction to Neural Architecture Search (NAS) and Heterogeneous Environments
Neural Architecture Search (NAS) is an automated method for designing neural network architectures that meet specific performance goals, such as accuracy, latency, or energy consumption. Traditionally, crafting models required expert knowledge and lengthy trial and error. NAS streamlines this process by exploring a vast space of potential designs and selecting those best suited to the target deployment scenario. This becomes especially valuable when deploying large language models (LLMs) for inference, where balancing computational resources and responsiveness is critical.
NAS Tailored for Heterogeneous Hardware
Heterogeneous computing environments combine different types of processors or accelerators — such as CPUs, GPUs, Neural Processing Units (NPUs), and Compute-In-Memory (CIM) architectures — each with unique strengths and trade-offs. These mixed setups aim to optimize overall system efficiency but complicate the task of model deployment because performance varies widely across hardware types.
Recent advances show that NAS frameworks can be customized for such heterogeneous systems. For example, the H4H-NAS framework specifically targets hybrid Convolutional Neural Network (CNN) and Vision Transformer (ViT) models optimized for combined NPU and CIM architectures at the edge. By jointly considering accuracy and hardware-specific metrics like latency and energy consumption, H4H-NAS generates architectures that outperform manually designed models in both effectiveness and efficiency. This approach highlights how NAS does not only improve model quality but also informs hardware-aware design decisions in mixed-device environments (source).
Optimizing Inference Pipelines on Heterogeneous Clusters
Beyond model design, deploying LLM inference on clusters that contain multiple types of AI accelerators presents additional challenges. Systems must optimize scheduling and resource allocation to avoid bottlenecks caused by uneven processing speeds or capabilities. One solution introduces a request scheduling mechanism that accounts for each instance’s unique processing profile, maximizing throughput while maintaining cost efficiency.
Experiments with this approach have demonstrated throughput improvements of up to 122.5%, showing that intelligent orchestration of heterogeneous resources yields faster and more economical inference pipelines (source). This kind of heterogeneity-aware scheduling complements NAS by ensuring that the custom architectures generated are deployed in the most productive way possible.
Frameworks for Heterogeneity-Aware Serving
On the software infrastructure side, frameworks like Hercules focus on serving personalized machine learning models in heterogeneous datacenter environments. By combining offline profiling with gradient-based scheduling and dynamic online provisioning, Hercules adapts to varying workloads and hardware capacities in real time. The result is improved throughput with significantly reduced power consumption and infrastructure footprint.
This two-stage optimization process exemplifies how system-level strategies integrate with NAS-designed models to fully unlock the benefits of heterogeneous environments in practical deployments (source).
Summary
In summary, the intersection of NAS and heterogeneous hardware environments is enabling new opportunities for building custom LLM inference pipelines. NAS frameworks tailored for hybrid architectures produce models that are not only accurate but also latency and energy efficient. Coordinated scheduling and adaptive serving solutions then maximize the utilization of diverse hardware clusters. Together, these advances create a flexible and efficient foundation for deploying large-scale AI applications across a variety of computational platforms.
Challenges in Large Language Model Inference
Large Language Models (LLMs) demand substantial computational resources, which introduces several challenges during inference—especially when deployed in heterogeneous environments composed of different types of hardware accelerators. Key issues include balancing latency, throughput, energy efficiency, and accuracy while making the most of diverse hardware capabilities.
Computational Complexity and Resource Demands
LLMs often involve billions of parameters and require extensive matrix operations during inference. This translates to heavy computational loads and memory bandwidth requirements that typical edge or datacenter hardware struggles to meet efficiently. As a result, latency can suffer, leading to slower response times for real-time applications. Energy consumption is another critical concern, since high computational intensity directly impacts power usage and operational costs.
Hardware Heterogeneity
Deploying LLM inference pipelines on heterogeneous clusters—systems consisting of different types of AI accelerators like Neural Processing Units (NPUs), Compute-In-Memory (CIM) blocks, GPUs, and CPUs—complicates scheduling and resource management. Each accelerator type varies in processing speed, memory architecture, and power consumption, which makes uniform model execution inefficient. Optimizing how workload is distributed across these diverse components requires careful consideration of their unique characteristics.
Trade-offs Between Model Accuracy and Efficiency
Achieving high accuracy while maintaining efficient inference is a central challenge. Larger and more complex models generally deliver better results but increase latency and energy cost. Reducing model size or precision can improve performance and power consumption but risks degrading output quality. Navigating these trade-offs effectively is essential for practical deployments.
Need for Custom Inference Pipelines
Traditional, one-size-fits-all inference approaches are insufficient for heterogeneous environments. A custom pipeline that adapts both the model architecture and the execution strategy to the underlying hardware profile is crucial. Recent work on Neural Architecture Search (NAS) shows promise by automatically designing hybrid models tailored to specific hardware combinations, thereby improving accuracy and efficiency simultaneously (source).
Scheduling and Throughput Optimization
Apart from model optimization, intelligent scheduling mechanisms that consider the processing capabilities of heterogeneous instances enhance throughput significantly. By dynamically allocating requests to the most suitable accelerators and balancing workloads intelligently, systems can achieve throughput gains exceeding 100%, reducing latency and cost (source).
System-Level Optimization
Frameworks like Hercules demonstrate the benefits of combining offline profiling, gradient-based scheduling search, and adaptive online provisioning to optimize personalized recommendation inference over heterogeneous datacenters. This two-stage approach not only boosts throughput but also lowers power consumption and infrastructure requirements (source).
In summary, LLM inference in heterogeneous environments must overcome challenges related to computational intensity, hardware diversity, and efficiency trade-offs. Solutions that integrate NAS-driven model design, heterogeneity-aware scheduling, and system-level optimization provide a path toward custom pipelines that balance accuracy, latency, throughput, and energy consumption in practical deployments.
Neural Architecture Search for Hybrid Models
Neural Architecture Search (NAS) has become a critical tool for designing models that efficiently utilize heterogeneous hardware platforms, especially in the context of large language model (LLM) inference. One promising development is the H4H-NAS framework, which specifically targets hybrid CNN/ViT architectures optimized for edge systems that combine Neural Processing Units (NPUs) and Compute-In-Memory (CIM) architectures. This approach acknowledges that heterogeneous hardware presents unique design challenges: balancing computational capabilities, memory access patterns, and energy constraints requires careful co-optimization of both model architecture and system configuration.
H4H-NAS sets itself apart by explicitly searching for model architectures that fit the strengths of hybrid hardware. By doing so, it achieves an improved trade-off between accuracy, latency, and energy efficiency when compared to standard architectures deployed on the same hardware. Unlike traditional NAS approaches that often focus solely on accuracy or speed in isolation, H4H-NAS integrates energy consumption metrics into its search criteria. This holistic optimization results in hybrid CNN/ViT models that are more efficient in real-world deployments on heterogeneous edge platforms (source).
The hybrid nature of these models—combining convolutional neural networks (CNNs) with vision transformers (ViTs)—leverages the strengths of both architectures. CNNs handle local pattern extraction efficiently, while transformers capture global context. NAS guides the ideal blend and configuration of these components tailored to the hardware, mitigating bottlenecks common in running diverse workloads on NPUs and CIM units.
This co-design philosophy—integrating neural architecture search directly with hardware characteristics—underscores a broader trend in efficient AI system design. It moves beyond one-size-fits-all models, enabling custom LLM inference pipelines that are finely tuned for the specific compute fabrics they run on, maximizing throughput and minimizing power draw. Such approaches are crucial for scaling large models in edge settings where resource constraints are significant and heterogeneous compute resources are the norm.
In summary, the H4H-NAS framework exemplifies how NAS can facilitate the construction of hybrid models that unlock the potential of heterogeneous hardware, aligning model innovation with hardware efficiency goals in LLM inference pipelines (source).
Overview of the H4H-NAS Framework
The H4H-NAS framework represents a distinct approach within neural architecture search (NAS) focused on designing hybrid convolutional neural network (CNN) and vision transformer (ViT) models. What sets this framework apart is its specific targeting of heterogeneous edge computing environments that combine Neural Processing Units (NPUs) and Compute-In-Memory (CIM) architectures. This heterogeneous hardware setup is increasingly common as edge systems demand both high efficiency and flexibility across different types of AI accelerators. H4H-NAS automates the creation of hybrid models that are tailored to the strengths and constraints of such environments, rather than relying on generic architectures. This targeted design improves both the accuracy of the models and their operational metrics, notably latency and energy efficiency, which are critical factors for edge deployment (source).
Key Design Principles
At its core, the H4H-NAS framework adheres to several crucial design principles:
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Hardware-Aware Architecture Search: Unlike traditional NAS approaches that optimize purely for accuracy or general resource usage, H4H-NAS incorporates detailed hardware models of NPUs and CIMs during the search. This hardware-awareness ensures that the hybrid CNN/ViT models produced align with the performance characteristics of the target heterogeneous systems, minimizing inference time and power draw.
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Hybrid Model Composition: By blending CNNs and ViTs, the framework exploits the complementary strengths of these architectures. CNNs excel at extracting local features efficiently, which suits the parallelism of NPUs, while ViTs offer global context modeling that complements the memory-centric operations in CIM. The NAS engine intelligently balances these components for optimal system-level performance.
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Joint Model and System Optimization: H4H-NAS does not treat model design and system design as separate steps. Instead, it guides both simultaneously, creating a feedback loop where hardware constraints and model architecture co-evolve. This synergy results in models that are not only accurate but also optimized for the specific resource and throughput profiles of the heterogeneous edge environment.
Impact on Latency and Energy Efficiency
The tight integration of hardware-aware NAS with hybrid CNN/ViT model design pays dividends in real-world metrics. Experiments with H4H-NAS have demonstrated significant reductions in latency compared to conventional model designs while also improving energy efficiency. This combination is especially valuable for scenarios like edge AI where power budgets are limited, and real-time inference is required. By leveraging its design principles, H4H-NAS effectively addresses the core challenges of heterogeneous environments — maximizing performance while minimizing resource consumption (source).
In summary, the H4H-NAS framework exemplifies how NAS can be adapted beyond pure model accuracy optimization to incorporate heterogeneous hardware constraints directly into the search process. Its hybrid model approach and co-optimization of system and architecture make it a powerful tool for deploying advanced LLM inference pipelines in complex, resource-diverse settings.
Optimizing Hybrid CNN/ViT Models for Edge Systems
A significant challenge in deploying large language models (LLMs) on edge systems lies in balancing computational efficiency with model accuracy under strict latency and energy constraints. Recent work employing neural architecture search (NAS) offers promising solutions, particularly through the design of hybrid CNN and Vision Transformer (ViT) models tailored for heterogeneous edge hardware.
Leveraging NAS for Hybrid Architectures
The H4H-NAS framework exemplifies how NAS can generate optimized hybrid CNN/ViT models specifically for heterogeneous edge systems that combine Neural Processing Units (NPUs) with Compute-In-Memory (CIM) architectures. By jointly exploring model architectures and system-level deployment factors, H4H-NAS improves both accuracy and inference efficiency. This co-design approach not only accelerates model inference times but also achieves notable reductions in energy consumption, critical for edge environments with limited power budgets. The search process incorporates hardware-aware constraints to navigate the trade-offs between convolutional and transformer components, dynamically adapting model structures to the capabilities and bottlenecks of the target heterogeneous platform. This methodology effectively bridges the gap between model complexity and real-world deployment demands (source).
System-Aware Design Principles
Optimizing hybrid CNN/ViT models for edge systems goes beyond model architecture. The integration of NAS results with hardware characteristics and deployment scheduling is essential. By profiling the heterogeneous computing resources and embedding this information into the NAS loop, such frameworks enable a more precise matching of model operations to available accelerators. This holistic approach facilitates throughput maximization and latency minimization simultaneously. For instance, deployment strategies that consider the division of workloads across NPUs and CIM units prevent idle hardware phases and mitigate communication overhead, leading to improved inference efficiency. These system-aware optimization principles ensure that the resulting hybrid models not only perform well on benchmarks but also sustain performance in practical edge scenarios (source).
Impact on Edge LLM Inference Pipelines
The advancements in hybrid CNN/ViT model optimization via NAS have direct implications for LLM inference pipelines at the edge. By achieving a better balance between Transformer-based attention mechanisms and convolutional feature extraction, these models improve the expressivity needed for natural language tasks while respecting resource constraints. When deployed within heterogeneously structured edge clusters, such optimized models enhance task throughput and energy efficiency, enabling more responsive and scalable LLM applications. This evolution reflects a broader trend where neural architecture search, combined with runtime scheduling and hardware profiling, creates adaptable inference pipelines that harness heterogeneous edge infrastructure more effectively (source, source).
Benefits: Accuracy, Latency, and Energy Efficiency Improvements
When deploying large language models (LLMs) in heterogeneous environments, the integration of Neural Architecture Search (NAS) with hardware-aware optimizations delivers substantial benefits across accuracy, latency, and energy consumption.
Accuracy Enhancements Through NAS-Designed Architectures
One notable advancement is the development of the H4H-NAS framework, which leverages NAS to design hybrid models combining convolutional neural networks (CNNs) and vision transformers (ViTs). This design specifically targets heterogeneous edge systems that utilize both Neural Processing Units (NPUs) and Compute-In-Memory (CIM) architectures. By tailoring the network architecture to the underlying hardware, H4H-NAS achieves higher accuracy without sacrificing efficiency. This is critical in LLM inference, where maintaining model fidelity is as important as meeting operational constraints. The focused search for architectures adapted to mixed hardware setups ensures that the performance gains are sustainable in real-world environments (source).
Latency Improvements with Optimized Scheduling and Deployment
Latency reduction emerges as a direct consequence of carefully combining NAS with heterogeneity-aware scheduling mechanisms. In large-scale LLM inference over clusters composed of diverse AI accelerators, an intelligent scheduling system that accounts for each instance’s processing capabilities can achieve up to 122.5% throughput improvements. This scheduling alongside optimized deployment configurations minimizes wait times and balances workloads effectively, resulting in significantly faster task completion. By understanding and exploiting the computational strengths and weaknesses of different hardware components, the system ensures that latency remains low across varied tasks (source).
Energy Efficiency Gains from Hardware-Aware Model and System Design
Energy consumption is a critical factor when running LLM inference at scale. The Hercules framework exemplifies how a two-stage optimization—offline profiling and online provisioning—can lead to substantial energy savings. By applying gradient-based scheduling searches during offline phases and adapting resource allocation online, Hercules reduces power usage while boosting throughput capacity. The approach highlights the benefits of co-designing models and serving platforms with full awareness of heterogeneous datacenter hardware characteristics, enabling efficient resource utilization and lower operational costs (source).
Summary
Combining neural architecture search with heterogeneity-aware system design and intelligent scheduling unlocks improvements in accuracy, latency, and energy efficiency. These advantages enable custom LLM inference pipelines to run optimally across diverse environments and hardware platforms, balancing the demands of performance and resource consumption effectively.
High-Throughput LLM Inference on Heterogeneous Clusters
Deploying large language model (LLM) inference at scale often involves handling heterogeneous clusters composed of various AI accelerators. These clusters might include different types of GPUs, NPUs, and specialized hardware like Compute-In-Memory (CIM) units. This hardware diversity, while offering potential performance benefits, complicates resource allocation and request scheduling, which are critical for achieving high throughput.
A recent approach to these challenges proposes an optimized deployment framework that carefully matches LLM inference workloads to the heterogeneous processing capabilities of individual cluster instances. Instead of static assignment, this system dynamically configures deployment parameters and schedules inference requests based on real-time profiling of each node’s throughput and latency. The key insight is to balance loads according to the unique strengths and bottlenecks across the diverse accelerators rather than treating the cluster as a homogeneous pool.
Experimental results have shown this heterogeneity-aware deployment and scheduling can improve throughput significantly—up to 122.5% higher compared to naïve or homogeneous scheduling methods. This not only increases task processing speed but also drives better cost efficiency by maximizing utilization of more specialized hardware without overloading less capable nodes (source).
Design Considerations and Scheduling Strategies
The system employs an intelligent request scheduling mechanism that factors in the processing capabilities and queue lengths of different cluster instances. By profiling these metrics offline, it builds a performance model used to inform real-time decisions. This approach ensures that requests are dispatched to the most appropriate hardware, improving overall throughput and latency.
Underpinning this strategy is the recognition that heterogeneous environments require holistic co-optimization of model deployment and cluster resource management. Combining architectural optimizations with scheduling algorithms tailored to the cluster’s hardware makeup enables LLM inference pipelines to scale efficiently without sacrificing responsiveness (source).
Impact on Infrastructure and Energy Efficiency
Beyond throughput, this approach also leads to infrastructure savings and reduced power consumption by preventing hardware underutilization and avoiding unnecessary queue build-up on slower nodes. Similar benefits have been observed in related heterogeneous inference frameworks like Hercules, which focus on personalized recommendation models but underline the broader applicability of heterogeneity-aware scheduling in large-scale AI serving (source).
Together with neural architecture search methods that generate models optimized for hybrid hardware, these advances form a cohesive strategy for designing custom LLM inference pipelines that fully leverage heterogeneous clusters. The result is a balance of accuracy, latency, throughput, and energy use tuned to diverse deployment environments (source).
Challenges of Deploying LLM Inference on Diverse AI Accelerators
Deploying large language model (LLM) inference across a range of AI accelerators presents multiple challenges stemming from hardware diversity, performance variability, and resource management complexity. The heterogeneous nature of accelerators—including NPUs, GPUs, CIM devices, and others—forces developers to navigate trade-offs in throughput, latency, power consumption, and accuracy that vary significantly by device type and model architecture.
Hardware Diversity and Model Compatibility
One major challenge is ensuring model compatibility and efficiency across different types of AI hardware. Each class of accelerator offers distinct computational paradigms; for example, Neural Processing Units (NPUs) may excel at dense matrix operations while Compute-In-Memory (CIM) architectures optimize energy efficiency by minimizing data movement. This diversity demands model designs that are flexible and hardware-aware. Approaches like the H4H-NAS framework demonstrate how neural architecture search can tailor hybrid CNN/ViT models explicitly for such heterogeneous environments, balancing accuracy with latency and energy needs (source).
Without such co-design, a model optimized for one accelerator could perform poorly or inefficiently on another, complicating deployment scenarios where multiple types coexist.
Scheduling and Throughput Optimization
Another key difficulty lies in maximizing throughput when inference workloads are spread over heterogeneous clusters. Different accelerators exhibit varying processing speeds and capacities, which can result in bottlenecks or underutilized hardware if scheduling strategies are not carefully designed. Systems that introduce adaptive request scheduling, sensitive to each instance’s capabilities, achieve significant throughput gains—up to 122.5% improvement has been shown in experimental setups (source).
Such intelligent scheduling must dynamically balance task assignment across heterogeneous resources, accounting for each accelerator’s workload, communication overhead, and real-time availability.
Infrastructure and Energy Efficiency
Deploying LLMs on diverse accelerators also influences infrastructure utilization and energy consumption. Heterogeneity-aware serving frameworks incorporate offline profiling and gradient-based scheduling to optimize cluster provisioning and reduce waste. In the Hercules framework case, this approach led to not only improved throughput but also substantial power savings and infrastructure capacity reductions (source).
Efficiently scaling LLM inference thus requires system-level optimizations that go beyond model accuracy or raw speed, considering energy footprint and hardware cost-effectiveness as integral parts of deployment strategy.
Together, these challenges highlight the need for integrated solutions that co-design models and system software. Leveraging neural architecture search tuned for heterogeneous hardware alongside adaptive scheduling and cluster management forms a promising path toward deploying LLM inference pipelines that are both performant and resource-efficient across diverse AI accelerators.
Optimization of Deployment Configurations and Request Scheduling
Deploying large language model (LLM) inference pipelines efficiently across heterogeneous environments requires careful optimization of both deployment configurations and request scheduling. These are critical for maximizing throughput, minimizing latency, and ensuring energy-efficient operation, especially when dealing with diverse hardware such as Neural Processing Units (NPUs), Compute-In-Memory (CIM) architectures, and various AI accelerators.
Optimizing Deployment for Heterogeneous Hardware
Recent work on hybrid models such as the H4H-NAS framework demonstrates the potential of tailoring neural architectures to heterogeneous edge systems that combine different processing units. By co-optimizing model design alongside system constraints, H4H-NAS produces hybrid CNN/ViT architectures that improve accuracy while significantly reducing latency and energy consumption. This joint approach to architecture search and deployment configuration guides better utilization of NPUs and CIMs, leading to balanced workloads across these heterogeneous components and efficient resource use (source).
Similarly, frameworks focusing on personalized recommendation systems, like Hercules, incorporate heterogeneity-aware scheduling and offline profiling to determine optimal resource provisioning. This process uses gradient-based scheduling searches combined with an online serving cluster setup to dynamically allocate hardware resources. The result is boosted throughput and decreased power consumption, highlighting the gains from profiling and adaptive deployment in heterogeneous datacenters (source).
Intelligent Request Scheduling to Maximize Throughput
Beyond static deployment optimization, intelligent request scheduling algorithms play a crucial role in handling varied AI accelerator capabilities within clusters. One approach involves a scheduling mechanism that considers the processing speeds and capacities of different instances, dynamically dispatching inference requests to maximize parallelism and throughput. Experiments show that such heterogeneity-aware request scheduling can increase throughput by over 120%, significantly improving processing speed and cost efficiency without compromising model performance (source).
This scheduling strategy ensures that bottlenecks caused by slower accelerators are minimized and that faster units are fully utilized. In practice, this means the system adapts in real-time to hardware availability and workload characteristics, effectively balancing inference tasks across the cluster.
Integrating NAS with Deployment and Scheduling
The synergy of neural architecture search, optimized deployment, and advanced scheduling enables custom LLM inference pipelines that meet specific hardware constraints while achieving strong performance metrics. NAS frameworks like H4H-NAS not only produce efficient models but also inform deployment decisions tailored to heterogeneous hardware profiles. In turn, scheduling mechanisms leverage these configurations to dynamically optimize workload distribution, ensuring throughput and energy efficiency at scale. Together, these strategies represent a promising direction for scalable, cost-effective LLM inference in increasingly complex and heterogeneous computing environments.
Throughput Improvements through Heterogeneity-Aware Scheduling
One of the most significant gains achieved by leveraging neural architecture search (NAS) and heterogeneity-aware scheduling in LLM inference pipelines is the dramatic improvement in throughput. A recent approach targeting clusters composed of diverse AI accelerators uses optimized deployment configurations paired with a request scheduling mechanism designed to capitalize on each instance's unique processing capabilities. This fine-grained scheduling efficiently balances workloads according to the hardware’s characteristics, achieving throughput gains of up to 122.5% compared to traditional homogeneous scheduling strategies. Such improvement not only speeds up task processing but also enhances the overall responsiveness of LLM applications deployed across heterogeneous systems (source).
Cost Efficiency via Infrastructure and Energy Savings
Beyond throughput, cost efficiency gains are strongly evident in systems that incorporate NAS for hybrid model design and heterogeneity-aware provisioning. The H4H-NAS framework, for example, generates hybrid CNN/ViT models tailored for heterogeneous edge deployments combining NPUs and Compute-In-Memory architectures. This targeted model design improves accuracy while significantly reducing latency and energy consumption, which together contribute to lowering operational costs. Similarly, the Hercules framework’s two-stage optimization process—offline profiling followed by online provisioning—ensures infrastructure is utilized optimally, leading to substantial capacity savings and reduced power usage in datacenter environments. These optimizations make large-scale LLM inference both practical and scalable in cost-constrained, hardware-diverse settings (source, source).
Balancing Performance Metrics in Heterogeneous Environments
The combined use of NAS, scheduling optimizations, and heterogeneity-aware design addresses multiple performance axes: accuracy, latency, throughput, and energy use. Custom model architectures derived from NAS adapt to the specific capabilities of hybrid hardware setups, optimizing the trade-offs between computational cost and predictive performance. Meanwhile, intelligent scheduling and cluster provisioning maximize system utilization without over-provisioning resources, which is critical in heterogeneous environments where diverse hardware types coexist. Together, these mechanisms lead to inference pipelines that are not only faster and cheaper but also more precise and energy-efficient, underscoring the growing importance of adaptive, hardware-aware optimization techniques in modern LLM deployments (source, source, source).
Heterogeneity-Aware Inference Serving with Hercules Framework
Deploying custom large language model (LLM) inference pipelines on heterogeneous datacenters presents significant challenges due to diverse hardware capabilities and varying resource constraints. The Hercules framework addresses these challenges by incorporating a heterogeneity-aware design that optimizes both resource utilization and inference performance.
Hercules achieves this through a two-stage optimization approach. The first stage involves offline profiling, where detailed performance metrics of the deployed recommendation models are collected across different hardware configurations. This profiling informs a gradient-based scheduling search algorithm that identifies optimal resource allocation and task scheduling strategies tailored to the heterogeneous environment. By understanding each hardware node’s processing capabilities and bottlenecks during this stage, Hercules can preemptively adjust deployment to maximize efficiency.
In the second stage, Hercules performs online cluster provisioning. This ensures that the previously determined scheduling strategies are dynamically applied as workloads fluctuate, maintaining high throughput without overprovisioning resources. The interplay between offline profiling and online resource adaptation creates a feedback loop that continuously refines inference serving under real-world conditions.
The practical results of using Hercules are notable. Studies show improvements in throughput and significant infrastructure capacity savings, meaning fewer physical resources are necessary to meet performance requirements. Additionally, the framework reduces power consumption, which is critical for sustainable AI infrastructure. These gains directly address the typical trade-offs between latency, accuracy, throughput, and energy efficiency found in heterogeneous LLM inference pipelines.
By focusing on personalized recommendation models, a common use case for LLMs, Hercules demonstrates the benefits of a heterogeneity-aware framework in real datacenter scenarios. Its approach illustrates how combining offline optimization techniques with adaptive online provisioning can unlock the full potential of mixed hardware clusters, making it easier to deploy efficient, scalable inference pipelines in diverse computational environments (source).
Overall, Hercules provides a concrete example of how heterogeneity-aware inference serving can be systematically achieved, setting a precedent for future frameworks that leverage neural architecture search and adaptive scheduling in complex, heterogeneous AI ecosystems.
Two-Stage Optimization: Offline Profiling and Online Provisioning
Optimizing LLM inference pipelines in heterogeneous environments involves a careful balance of accuracy, latency, throughput, and energy efficiency. A practical strategy emerging from recent research is the two-stage optimization approach, which separates the problem into offline profiling and online provisioning phases. This approach is essential for effectively managing complex hardware setups where resources and workloads vary dynamically.
Offline Profiling with Neural Architecture Search and Scheduling
The first stage focuses on offline profiling, where detailed performance characterization of the system takes place. This phase leverages sophisticated techniques like Neural Architecture Search (NAS) to explore and identify the most efficient model architectures suited for a given heterogeneous hardware environment. For example, the H4H-NAS framework designs hybrid CNN/ViT models optimized for systems that combine Neural Processing Units (NPUs) and Compute-In-Memory (CIM) architectures. By conducting this exploration offline, the system can evaluate many candidate models and configurations, measuring trade-offs between accuracy, latency, and energy use without impacting live operations (source).
Complementing NAS, gradient-based scheduling searches play a crucial role in profiling how different deployment configurations impact system performance. This profiling step records metrics like throughput and resource utilization for various scheduling policies, forming a detailed map of system behavior under diverse conditions (source).
Online Provisioning for Adaptive Resource Management
The second stage moves into the online realm, where real-time provisioning adapts resource allocation and request scheduling based on the profiling data collected earlier. By dynamically selecting the most suitable deployment configurations, the system can maximize throughput and efficiency according to current workload demands and hardware availability. For instance, in heterogeneous clusters running LLM inference, online provisioning involves optimizing the assignment of requests to accelerators with varying capacities, which substantially boosts throughput—reported gains reach up to 122.5%—while reducing costs and power consumption (source).
Frameworks like Hercules demonstrate this concept by applying the two-stage approach to personalize recommendation models on heterogeneous datacenters. The offline profiling phase informs the gradient-based scheduler about optimal resource allocations, which the online service then uses to provision clusters efficiently. This method not only improves throughput but also yields significant infrastructure savings and lowers overall power usage (source).
By dividing optimization into dedicated offline and online phases, developers can more effectively harness the complexity of heterogeneous hardware, achieving finely tuned LLM inference pipelines that balance competing demands in real-world deployment scenarios.
Impact on Throughput
One of the most significant benefits observed when leveraging neural architecture search (NAS) in heterogeneous inference pipelines is the notable improvement in throughput. Systems that integrate NAS with awareness of hardware diversity can tailor model architectures and deployment strategies to match the specific processing capabilities of heterogeneous clusters. For instance, a scheduling mechanism that accounts for differences in AI accelerator performance can redistribute inference workloads more effectively across available instances, leading to throughput improvements exceeding 120% in some cases. This kind of throughput boost not only accelerates individual task processing but also increases overall system responsiveness, making LLM inference viable for latency-sensitive applications (source).
Infrastructure Savings through Optimized Resource Use
By finely tuning models with NAS frameworks like H4H-NAS, which design hybrid CNN/ViT architectures optimized for edge devices combining NPUs and Compute-In-Memory hardware, systems can significantly reduce the strain on infrastructure. These optimized models require less computational overhead while maintaining or even improving accuracy. This means fewer or smaller hardware resources are needed to deliver the same or better performance. Similarly, frameworks like Hercules employ gradient-based scheduling and provisioning that adapt server resources dynamically, leading to substantial infrastructure capacity savings. This not only reduces capital expenditure but also simplifies scaling in heterogeneous datacenters (source, source).
Power Reduction from Energy-Efficient Design
Energy efficiency is a critical concern in deploying large language models at scale. NAS-driven approaches help reduce power consumption by automating the search for architectures that balance accuracy and computational efficiency tailored to the hardware in use. Hybrid models designed through NAS frameworks consume significantly less energy during inference by exploiting architectural features that align with the strengths of NPUs and CIM units. Additionally, heterogeneity-aware serving solutions that optimize cluster provisioning further cut power usage by shutting down or scaling back underutilized resources. The combined effect is a meaningful drop in overall power consumption, making custom LLM inference pipelines more sustainable and easier to operate in power-sensitive environments (source, source).
Together, these improvements in throughput, infrastructure efficiency, and power reduction showcase how NAS and heterogeneity-aware system design can transform LLM inference from a resource-intensive task into a more practical and scalable service across diverse hardware environments.
Integrating NAS, Scheduling, and System Design for Custom LLM Pipelines
Building efficient large language model (LLM) inference pipelines in heterogeneous environments requires a harmonious integration of neural architecture search (NAS), intelligent scheduling, and system-level design. Each component addresses different facets of performance and resource management, and their joint optimization is key to achieving balanced accuracy, latency, throughput, and energy consumption.
Neural Architecture Search for Hybrid Models
One of the primary innovations in recent research is the use of NAS frameworks tailored to heterogeneous hardware. The H4H-NAS framework exemplifies this by designing hybrid convolutional neural network (CNN) and vision transformer (ViT) models specifically for edge systems that combine Neural Processing Units (NPUs) and Compute-In-Memory (CIM) technologies. This approach not only boosts model accuracy but also significantly reduces latency and energy usage compared to traditional designs. The NAS process informs both the model’s structural choices and guides hardware-aware optimizations, ensuring the architecture fits the constraints and capabilities of heterogeneous environments (source).
Scheduling for Throughput Maximization
Optimizing deployment across clusters with diverse AI accelerators demands advanced scheduling solutions. By profiling instance processing capabilities and adapting request scheduling accordingly, systems can substantially improve throughput and cost efficiency. For instance, recent work demonstrated throughput boosts of up to 122.5% by customizing the scheduling strategy to heterogeneous clusters. This approach enables more effective resource utilization and faster task execution, crucial for real-time or large-scale LLM inference tasks (source).
Heterogeneity-Aware System Design
Beyond model and scheduling innovations, the overall system design must incorporate heterogeneity awareness at multiple levels. The Hercules framework, targeting personalized recommendation models in heterogeneous datacenter settings, uses a two-stage optimization pipeline. It begins with offline profiling combined with gradient-based scheduling search, followed by dynamic online provisioning of cluster resources. This integrated strategy results in higher throughput, infrastructure savings, and reduced power consumption, demonstrating how system design can synergize with NAS and scheduling to optimize pipeline performance under varying hardware conditions (source).
Holistic Integration Benefits
By combining NAS to tailor architecture, scheduling algorithms to maximize hardware throughput, and heterogeneity-aware system designs, LLM inference pipelines can achieve superior performance metrics across heterogeneous environments. These integrated efforts allow pipelines to exploit the full capabilities of diverse computing resources while managing trade-offs between speed, accuracy, energy consumption, and cost. This holistic approach is increasingly essential for deploying custom LLM solutions at scale in complex, resource-diverse settings.
Balancing Accuracy, Latency, Throughput, and Energy in Heterogeneous Settings
Building efficient LLM inference pipelines in heterogeneous environments requires a delicate balance between multiple performance metrics: accuracy, latency, throughput, and energy consumption. Each dimension presents its own challenges, which must be addressed collectively to optimize real-world application performance.
Optimizing Model Architecture for Accuracy and Efficiency
One effective approach is leveraging Neural Architecture Search (NAS) to tailor models that fit the diverse hardware profile of heterogeneous systems. The H4H-NAS framework exemplifies this by generating hybrid CNN/ViT architectures designed specifically for edge systems combining Neural Processing Units (NPU) and Compute-In-Memory (CIM) architectures. This co-design not only improves model accuracy but also significantly reduces latency and energy consumption. The tight coupling of NAS with specific hardware capabilities enables fine-grained trade-offs where model complexity is balanced against the practical constraints of deployment hardware (source).
Scheduling and Resource Allocation for Maximizing Throughput
Balancing throughput and latency becomes more complex in a distributed environment where clusters consist of heterogeneous AI accelerators with varied computation speeds. A scheduling mechanism that is aware of these differences is crucial. For example, systems that optimize request scheduling by considering instance-specific processing capabilities can yield throughput gains exceeding 120%, as demonstrated in recent research. This heterogeneous-aware deployment strategy not only speeds up task completion but also improves cost efficiency by maximizing resource usage without over-provisioning (source).
Two-Stage Optimization for Serving Pipelines
To further enhance throughput and reduce power consumption in large-scale deployments, frameworks like Hercules adopt a two-stage optimization approach. Offline profiling with gradient-based search helps identify optimal scheduling policies tailored to heterogeneous datacenter resources. Then, online provisioning dynamically adjusts cluster resources according to workload demands, achieving significant infrastructure savings and energy reductions. This dynamic and feedback-driven approach allows inference pipelines to maintain quality of service while efficiently using available hardware (source).
Integrating These Dimensions
The key insight across these advancements is that optimizing LLM inference pipelines in heterogeneous environments is not about maximizing any single metric in isolation. Instead, effective systems integrate NAS-driven model optimization, heterogeneity-aware scheduling, and adaptive resource management to deliver balanced performance. Accuracy improvements are sustained while latency is minimized, throughput is maximized, and energy consumption is kept in check for a well-rounded, practical deployment. This holistic view is critical for scaling LLM inference on increasingly diverse hardware landscapes.
Conclusion: Future Directions and Implications for LLM Inference Optimization
The exploration of neural architecture search (NAS) combined with heterogeneous computing offers a promising avenue for advancing large language model (LLM) inference pipelines. As demonstrated by recent innovations, the synergy between NAS frameworks like H4H-NAS, heterogeneity-aware scheduling, and system-level optimization creates a foundation for much more efficient inference processes.
Advancing Model and System Co-Design
One of the key insights from current research is the importance of co-designing neural architectures alongside the underlying heterogeneous hardware environment. For instance, H4H-NAS exemplifies how NAS can tailor hybrid CNN/ViT models optimized for specific hardware such as NPUs and CIMs, striking a strong balance between accuracy, latency, and energy efficiency. This kind of fine-grained adaptation moves beyond one-size-fits-all approaches to leverage unique hardware capabilities, leading to meaningful performance gains (source).
Optimizing Deployment and Scheduling for Throughput and Cost
Efficient LLM inference is not only a question of model design but also how models are deployed and scheduled on diverse hardware clusters. The research on high-throughput inference under heterogeneous clusters highlights the advantage of intelligent request scheduling that is aware of instance capabilities. By dynamically optimizing deployment configurations and scheduling, the system achieved up to a 122.5% throughput increase, which translates into faster task processing and better utilization of heterogeneous resources (source).
Embracing Dynamic and Personalized Serving Environments
In production contexts, frameworks like Hercules reveal the potential of heterogeneity-aware serving that adapts to personalized workload demands. Through offline profiling combined with online provisioning, it efficiently balances resource allocation, leading to substantial infrastructure savings and reduced power consumption without sacrificing throughput. This approach underscores the need for adaptive inference pipelines that respond intelligently to shifting operational conditions (source).
Looking Forward: Integration and Automation
The convergence of NAS, heterogeneity-aware scheduling, and dynamic provisioning points toward future inference systems that are fully integrated and automated. Such systems could continuously refine their architectures and deployment strategies in response to real-time feedback, further optimizing across multiple dimensions: accuracy, latency, energy, and cost. Additionally, as hardware diversity continues to grow, robust abstraction layers and standardized interfaces for managing heterogeneity will be critical to scaling these optimizations.
In summary, the future of LLM inference hinges on holistic approaches that combine model innovation with hardware-aware, adaptive orchestration. These advances promise to unlock the full potential of large models in cost-effective, scalable, and sustainable ways across varied computing environments.